Systems and methods for optimizing maintenance plans in the presence of sensor data

ABSTRACT

Systems and methods for generating a sensor driven optimized maintenance program are provided. A first maintenance plan for an apparatus type is received; the first maintenance plan defining maintenance tasks for the apparatus type and defining intervals at which the maintenance tasks are to be performed. Historical sensor data, historical scheduled maintenance data, and historical unscheduled maintenance data are received and correlated to determine a predictive value of the sensor data. One or more maintenance optimization objectives of an operator of an apparatus of the apparatus type are received, and the sensor driven optimized maintenance program is generated, based at least on the predictive value of the sensor data and the maintenance optimization objectives. The sensor driven optimized maintenance program includes an adjusted one of the defined intervals.

BACKGROUND

Vehicle fleet operators follow maintenance plans, in order to providefor efficient operations and fleet utilization. Examples of suchvehicles include but are not limited to aircraft, air-cargo vehicles,automotive, unmanned aerial vehicles (UAV), maritime vehicles (ships,submarines, etc.), etc. In the case of an aircraft, maintenance planningdata (MPD) is often provided to the operator of an aircraft by themanufacturer, and typically includes information related to recommendedscheduled maintenance tasks, their intervals, required access, and otherrelevant information. Maintenance tasks often are grouped and performedin packages identified as “check packages”.

In some operational scenarios, operator uses the MPD as a starting pointand adjusts its maintenance plan schedule based on financialconsiderations, for example the cost of removing a vehicle from serviceand performing a particular check package. However, maintenance planschedule adjustments that are blind to actual operational conditions andavailable historical data, introduces the likelihood of performingmaintenance more often than is necessary (thereby increasing cost), orless often than is optimal to maintain operational risks below a targetacceptable maintenance plan that leverage historical maintenance dataand sensor data.

SUMMARY

The disclosed examples are described in detail below with reference tothe accompanying drawing figures listed below. The following summary isprovided to illustrate some examples disclosed herein. It is not meant,however, to limit all examples to any particular configuration orsequence of operations.

Some aspects and implementations disclosed herein are directed tomethods for generating a sensor driven optimized maintenance program(SDOMP). An example method comprises: receiving a first maintenance planfor an apparatus type, the first maintenance plan defining maintenancetasks for the apparatus type and defining intervals at which themaintenance tasks are to be performed; receiving historical sensor data(including sensor alert data), historical scheduled maintenance data,historical unscheduled maintenance data, and documentation linkingsensor alerts with maintenance tasks; correlating the historical sensordata with the historical scheduled maintenance data and the historicalunscheduled maintenance data to determine a predictive value of thesensor data; receiving one or more maintenance optimization objectivesof an operator of an apparatus of the apparatus type; and based at leaston the predictive value of the sensor data and the maintenanceoptimization objectives, generating the SDOMP, the generated SDOMPcomprising an adjusted one of the defined intervals.

Systems and methods for generating an SDOMP are also provided. A firstmaintenance plan for an apparatus type is received; the firstmaintenance plan defining maintenance tasks for the apparatus type anddefining intervals at which the maintenance tasks are to be performed.Historical sensor data, historical scheduled maintenance data, andhistorical unscheduled maintenance data are received and correlated todetermine a predictive value of the sensor data. One or more maintenanceoptimization objectives of an operator of an apparatus of the apparatustype are received, and the SDOMP is generated, based at least on thepredictive value of the sensor data and the maintenance optimizationobjectives. The SDOMP includes an adjusted one of the defined intervals.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed examples are described in detail below with reference tothe accompanying drawing figures listed below:

FIG. 1 is a block diagram of apparatus production and servicemethodology.

FIG. 2 is a block diagram of an apparatus 200 for various aspects of thedisclosure.

FIG. 3 is a schematic perspective view of a particular flying module201.

FIG. 4 is a block diagram of an implementation of a memory area 410 fora computing device 400.

FIG. 5 illustrates overlapping probability density curves and athreshold that drives probability of detection and probability of falsealarm values.

FIG. 6 illustrates a cumulative distribution function (CDF) for aprobability of detecting a condition requiring a repair or othermaintenance action.

FIG. 7 illustrates a changed threshold for the CDF curve of FIG. 6.

FIG. 8 illustrates graphically how the changed threshold of FIG. 7 isdetermined.

FIG. 9 is a block diagram of an implementation of a process flow forimplementing aspects of the disclosure.

FIG. 10 is an implementation alert display.

FIG. 11 is a block diagram of an implementation of a sensor mappingoperation.

FIG. 12 is an illustration of a timeline for related events, relevant ingenerating an SDOMP 430.

FIG. 13 illustrates relationships between non-routine findings andmaintenance messages.

FIGS. 14A-D illustrate the net benefit for various scenarios ofleveraging sensor data.

FIG. 15 is a flow chart illustrating an implementation of an operationof determining whether to escalate a maintenance task.

FIG. 16 is a flow chart illustrating an implementation of an operationof generating the SDOMP 430.

FIG. 17 is a block diagram illustrating a process for generating theSDOMP 430.

FIG. 18 is a block diagram illustrating an implementation of a systemsuitable for implementing various aspects of the disclosure.

Corresponding reference characters indicate corresponding partsthroughout the drawings.

DETAILED DESCRIPTION

The various embodiments will be described in detail with reference tothe accompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.References made throughout this disclosure relating to specificimplementations and implementations are provided solely for illustrativepurposes but, unless indicated to the contrary, are not meant to limitall implementations.

The foregoing summary, as well as the following detailed description ofcertain embodiments will be better understood when read in conjunctionwith the appended drawings. As used herein, an element or step recitedin the singular and preceded by the word “a” or “an” should beunderstood as not necessarily excluding the plural of the elements orsteps. Further, references to “one embodiment” are not intended to beinterpreted as excluding the existence of additional embodiments thatalso incorporate the recited features. Moreover, unless explicitlystated to the contrary, embodiments “comprising” or “having” an elementor a plurality of elements having a particular property could includeadditional elements not having that property.

Sensors onboard an apparatus are used with the goal of developingsensor-based prognostic tools to predict failures. This has thepotential to replace preventive maintenance with predictive maintenance.The sensors however, are typically deployed by component vendors ordesign engineers who often do not possess a complete knowledge ofinspection programs. Therefore, a functional mapping between sensors andmaintenance tasks enable improved inspection intervals through virtual(sensor-enabled) inspections. Unfortunately, due to the probabilisticnature of some sensor data, some operators could ignore alerts assumingthem to be false alarms. This hinders realization of the full potentialbenefits of the sensors. In general, a sensor alert is a subset ofsensor data, because some alerts are a binary condition of a sensormeasurement exceeding a threshold.

Probabilistic sensor data is convertible into objective, rigorous andactionable information in the form of revised inspection intervals,without the reliance on only operator interpretation of alerts. Thus,the benefits of the sensors are realized through reduced maintenancecost, improved efficiency and increased apparatus utilization, whensensors are at least partial predictors of failure or degradation.

Aspects of the present disclosure provide a computer implemented method,apparatus, and computer program product for generating a sensor drivenoptimized maintenance program (SDOMP). A first maintenance plan for anapparatus type is received; the first maintenance plan definingmaintenance tasks for the apparatus type and defining intervals at whichthe maintenance tasks are to be performed. Historical sensor data,historical scheduled maintenance data, and historical unscheduledmaintenance data are received and correlated to determine a predictivevalue of the sensor data. One or more maintenance optimizationobjectives of an operator of an apparatus (e.g., owner of the apparatusor fleet of the apparatus, administrator responsible for the apparatusor fleet of the apparatus, or a user operating the apparatus) of theapparatus type are received, and the SDOMP is generated, based at leaston the predictive value of the sensor data and the maintenanceoptimization objectives. The SDOMP includes an adjusted one of thedefined intervals.

For a particular apparatus (e.g., a type or model of a particularapparatus or fleet of apparatus), historic maintenance data includescosts that have been incurred for each maintenance event under anapproved maintenance plan (AMP). Historical maintenance data includeshistorical scheduled maintenance data and historical unscheduledmaintenance data. A maintenance event is any event that is associatedwith maintenance, repair, or replacement of a component of an apparatus.In one embodiment, a maintenance event includes, for example, afunctional part failure, a system failure, loss of function, decreasedfunction, service interrupt, corrosion, wear, slow response time,decreased efficiency, decreased fuel efficiency, loss of tire pressure,or any other event that necessitates maintenance, repair, or replacementof a component or subpart of a component. Further, each maintenanceevent includes one or more maintenance packages, and each maintenancepackage includes one more maintenance tasks. As used herein, amaintenance task is a task associated with inspecting, maintaining,repairing, and/or replacing a component or subcomponent. Using thehistoric maintenance data, the maintenance events, which include one ormore maintenance packages, are analyzed to determine a cost associatedwith each maintenance task.

Using sensor data along with historical maintenance data, costs, and oneor more maintenance optimization objectives (e.g., provided by anoperator of the apparatus or fleet of the apparatus), availableresources (e.g., workers and facility availability at any given time),and a defined length of time for a total operational life cycle of anapparatus (e.g., a defined length of time an operator owns the apparatusor fleet of the apparatus), each of the plurality of maintenance tasks,aspects of the present disclosure minimize the frequency of plannedmaintenance events and improves apparatus dispatch with optimizedscheduling of preventative planned maintenance tasks while minimizing anoccurrence of un-planned in-service maintenance tasks. For example, byincreasing an interval for a maintenance task (e.g., the number of daysbetween performing the maintenance task), that particular maintenancetask is performed on the apparatus or fleet of the apparatus a decreasednumber of times for the total operational life cycle the operator ownsthe apparatus or fleet of the apparatus (e.g., based on a leaseagreement). By postponing maintenance for the particular maintenancetask, the costs associated with performing the particular maintenance isalso postponed. For a leased apparatus or fleet of the apparatus, theincreased interval could result in the particular maintenance beingpostponed beyond the apparatus's lease return date. As such, theapparatus company would not incur the cost of the particular maintenancegiven the apparatus would be returned prior to the particularmaintenance being performed.

Scheduled maintenance activities are those maintenance activities thatare planned in advance, whereas unscheduled maintenance activities arethose activities that occur as a result of a failure or non-routinefinding. A routine finding is a condition that is detected or determinedto be as expected during an inspection (whether scheduled orunscheduled), for example a tread depth on a tire after a certain amountof usage. A non-routine finding is an unexpected condition that isdetected or determined during an inspection (whether scheduled orunscheduled), for example corrosion or deterioration of some componentin a state beyond what is expected and/or loss of functionality.

Referring more particularly to the drawings, embodiments of thedisclosure are described in the context of an apparatus of manufacturingand service method 100 as shown in FIG. 1 and apparatus 200 provided inFIG. 2. Turning first to FIG. 1, a diagram illustrating an apparatus(e.g., the carrying module 200) manufacturing and service method isdepicted in accordance with an embodiment. In one embodiment, duringpre-production, the apparatus manufacturing and service method 100includes specification and design 102 of the apparatus 200 in FIG. 2 andmaterial procurement 104. During production, component and subassemblymanufacturing 106 and system integration 108 of the apparatus 200 inFIG. 2 takes place. Thereafter, the apparatus 200 in FIG. 2 goes throughcertification and delivery 110 in order to be placed in service 112.While in service by a customer, the apparatus 200 in FIG. 2 is scheduledfor routine maintenance and service 114, which, in one embodiment,includes modification, reconfiguration, refurbishment, and othermaintenance or service described herein.

In one embodiment, each of the processes of the apparatus manufacturingand service method 100 are performed or carried out by a systemintegrator, a third party, and/or an operator. In these implementations,the operator is a customer. For the purposes of this description, asystem integrator includes any number of apparatus manufacturers andmajor-system subcontractors; a third party includes any number ofvenders, subcontractors, and suppliers; and in one embodiment, anoperator is an owner of an apparatus or fleet of the apparatus, anadministrator responsible for the apparatus or fleet of the apparatus, auser operating the apparatus, a leasing company, a military entity, aservice organization, or the like.

With reference now to FIG. 2, the apparatus 200 is provided. As shown inFIG. 2, an example of the apparatus 200 is a flying apparatus 201, suchas an aerospace vehicle, aircraft, air cargo, flying car, and the like.As also shown in FIG. 2, a further example of the apparatus 200 is aground transportation apparatus 202, such as an automobile, a truck,heavy equipment, construction equipment, a boat, a ship, a submarine andthe like. A further example of the apparatus 200 shown in FIG. 2 is amodular apparatus 203 that comprises at least one or more of thefollowing modules: an Air module, a payload module and a ground module.The air module provides air lift or flying capability. The payloadmodule provides capability of transporting objects such as cargo or liveobjects (people, animals, etc.). The ground module provides thecapability of ground mobility. The disclosed solution herein is appliedto each of the modules separately or in groups such as air and payloadmodules, or payload and ground, etc. or all modules.

With reference now to FIG. 3, a more specific diagram of the flyingmodule 201 is depicted in which an embodiment is implemented. In thisexample, the flying module 201 is an aircraft produced by the apparatusmanufacturing and service method 100 in FIG. 1 and includes an airframe303 with a plurality of systems 304 and an interior 306. Implementationsof the plurality of systems 304 include one or more of a propulsionsystem 308, an electrical system 310, a hydraulic system 312, and anenvironmental system 314. However, other systems are also candidates forinclusion. Although an aerospace example is shown, differentadvantageous embodiments are applied to other industries, such as theautomotive industry, etc.

With reference now to FIG. 4, a block diagram of a computing device 400for generating an SDOMP is provided. In some implementations, thecomputing device 400 includes one or more processors 402, one or morepresentation components 404 and a memory area 410. The disclosedimplementations associated with the computing device 400 are practicedby a variety of computing devices, including personal computers,laptops, smart phones, mobile tablets, hand-held devices, consumerelectronics, specialty computing devices, etc. The disclosedimplementations are also practiced in distributed computingenvironments, where tasks are performed by remote-processing devicesthat are linked through a communications network. Further, while thecomputing device 400 is depicted as a seemingly single device, in oneembodiment, multiple computing devices work together and share thedepicted device resources. For instance, in one embodiment, the memoryarea 410 is distributed across multiple devices, the processor(s) 402provided are housed on different devices, and so on.

In one embodiment, the processor(s) 402 includes any quantity ofprocessing units that read data from various entities, such as thememory area 410. Specifically, the processor(s) 402 are programmed toexecute computer-executable instructions for implementing aspects of thedisclosure. In one embodiment, the instructions are performed by theprocessor, by multiple processors within the computing device 400, or bya processor external to the computing device 400. In someimplementations, the processor(s) 402 are programmed to executeinstructions such as those illustrated in the flowcharts discussed belowand depicted in the accompanying drawings. Moreover, in someimplementations, the processor(s) 402 represent an implementation ofanalog techniques to perform the operations described herein. Forexample, the operations are performed by an analog client computingdevice 400 and/or a digital client computing device 400.

The presentation component(s) 404 present data indications to anoperator (e.g., owner of the apparatus or fleet of the apparatus,administrator responsible for the apparatus or fleet of the apparatus,or a user operating the apparatus) or to another device. In oneimplementation, presentation components include a display device,speaker, printing component, vibrating component, etc. One skilled inthe art will understand and appreciate that computer data is presentedin a number of ways, such as visually in a graphical user interface(GUI), audibly through speakers, wirelessly between the computing device400, across a wired connection, or in other ways. In one embodiment,presentation component(s) 404 are not used when processes and operationsare sufficiently automated that a need for human interaction is lessenedor not needed. For example, in some embodiments, the process ofgenerating and implementing the SDOMP and changing unscheduled tasks toscheduled tasks and implementing the scheduled tasks is fully automatedwithout user intervention.

The memory area 410 stores apparatus data 412, an approved maintenanceprogram (AMP) 414, maintenance costs 416, maintenance optimizationobjectives 418 (provided by an operator), historical maintenance data420, sensor data 422, data mapping component 424, a data correlationcomponent 426, an analysis component 428, a SDOMP 430, a dataacquisition and pre-processing component 432, and other data and logic434. The apparatus data 412 includes an apparatus delivery date, anapparatus retirement date, and operating hours for a particular type ofapparatus or fleet of apparatus, and if leased, an apparatus's leasereturn date, clearance period, and lease terms (e.g., what maintenanceevent must be performed before returning the apparatus to the lessor).In one embodiment, the AMP 414 includes one or more of the following:maintenance planning data (MPD), apparatus specific maintenance tasks,an inspection event for the apparatus, labor hours spent, and accessrequirements during maintenance. In some implementations, the memoryarea 410 additionally stores a first improved maintenance program thatis a more optimized version of the AMP 414, although is subject toimprovement using the sensor data 422. The maintenance costs 416includes data relating to historic costs incurred by an operator for aplurality of maintenance tasks for an apparatus or fleet of theapparatus. And more specifically, the historic cost of each event storedin the maintenance costs 416 are based on known costs incurred from oneor more maintenance providers (e.g., a company that performed the taskassociated with the maintenance tasks that is provided in themaintenance costs 416), based on apparatus labor, materials, and thelike. The historical maintenance data 420 includes informationdescribing historic maintenance events that have been performed from theAMP 414, including historical scheduled maintenance data, and historicalunscheduled maintenance data.

The sensor data 422 includes sensor-based alerting system (SBAS)apparatus health management (AHM) alert data, which, in someimplementations includes data on measurable components, such as any orall of tire pressure, oxygen pressure, hydraulic fluid, auxiliary powerunit (APU) oil level, engine oil level, and the like. In someimplementations, SBAS alert data includes apparatus health management(AHM) alert data. The sensor data 422 also includes data from othersensors aboard or in communication with the apparatus. In embodiments,the sensor data 422 includes both historical sensor data and current(e.g., real time) sensor data. The data mapping component 424 includes amap of SBAS alert data to scheduled maintenance task findings and thelogic to create the map. The data correlation component 426 establishesa correlation between non-routine findings and preceding relevantmaintenance messages. The analysis component 428 is a softwarestatistical analysis tool that identifies an optimal event requirementfor planning maintenance on an apparatus or fleet of apparatus. In oneembodiment, the statistical analysis is implemented using known oravailable statistical application program, such as, but withoutlimitation, statistical analysis software (SAS). The SDOMP 430 is theresulting generated product. The data acquisition and the pre-processingcomponent 432, and the other data and logic 434 are used for processingand storing data in support of the data mapping component 424 and theanalysis component 428. For example, the other data and logic 434comprises the operation history for the apparatus, the end date to theSDOMP 430 (e.g., the lease end date), and data for determining a netbenefit of adjusting an interval (e.g., using maintenance costs 416).

FIG. 5 illustrates a plot 500 that provides overlapping probabilitydensity curves 502 and 504 and a threshold 506 that drives probabilityof detection and probability of false alarm values. The curve 502 showsthe sensor measurement output for a condition that should not result inan alert. The curve 504 shows the sensor measurement output for acondition that should result in an alert. Unfortunately, neither sensorsnor interpretation of measured sensor output is perfectly correct. As aresult, some situations that should not result in an alert produce asensor measurement output that is indistinguishable from the sensormeasurement output that should exist in other situations that so meritan alert, and thus, the curves 502 and 504 overlap. The threshold 506 isset such that it determines whether an alert is generated. If a sensedcondition exceeds the threshold 506, a sensor alert is issued;conversely, if a sensed condition falls below the threshold 506, nosensor alert is issued. This creates four possibilities: Region 512falls under the curve 502 and is below the threshold 506. Therefore, theregion 512 represents the situation that the condition should not resultin an alert and no alert is issued. This is a true negative, whichcorrectly has no alerts. Region 522 falls under the curve 502 and isabove the threshold 506. Therefore, the region 522 represents a falsepositive (e.g., a false alarm), which is a situation in which thecondition should not result in an alert, but an alert is issued. This isa false alarm situation. Region 514 falls under the curve 504 and isabove the threshold 506. Therefore, the region 514 represents a truepositive, which is a situation in which the condition should result inan alert and alert is issued. This is a correct detection situation.Region 524 falls under the curve 504 and is below the threshold 506.Therefore, the region 524 represents a false negative, which is asituation in which the condition should result in an alert, but an alertis not issued. This is also commonly identified as a missed detectionsituation. In one embodiment, the threshold 506 is adjusted upward ordownward to find a desired balance (e.g., an optimal threshold) betweena probability of detection (P_(D)) and a probability of false alarm(P_(FA)). P_(D) is represented by the region 514 and P_(FA) isrepresented by the region 522. In general, increasing the threshold 506reduces P_(FA), but at a cost of reducing PD.

FIG. 6 illustrates a cumulative distribution function (CDF) curve 602for a cumulative probability of detecting a condition requiring a repairor other maintenance action. In one implementation, the CDF curve 602 isobtained by applying statistical analysis methods to historicalinspection data, such as a maintenance record dataset for scheduledmaintenance findings, and logbook dataset for unscheduled maintenance.In another implementation, the CDF curve 602 is generated usingstatistical analysis scheduled maintenance optimization engine (SASMO),which is described in U.S. Pat. No. 8,117,007. The CFD curve 602 showsthe probability of a non-routine finding, and some implementations ofthe CDF curve 602 are obtained by analyzing the historical maintenancedata of an apparatus and its components. A maintenance action is, forexample, an unscheduled repair, whereas a maintenance task is, forexample, an inspection or preventive maintenance. If a non-routinefinding condition is not remedied in a timely manner, the result ispotentially an impending unscheduled maintenance action. The CDF curve602 is an estimate using the historical maintenance data 420 from one ormore operators, and represents the likelihood (probability) of detectinga condition requiring a repair or other maintenance action at someinspection time after a prior inspection had not identified such acondition. A given target acceptable risk level is indicated by line604. Where the line 604 intersects the CDF curve 602, this indicates aninspection interval T 606 in units of time. An inspection prior to theinterval T 606 is an early inspection and is less likely to result inthe identification of a condition requiring a repair or othermaintenance action. An inspection after the interval T 606 is a late(escalated) inspection and, under some situations, is more likely toresult in the identification of a condition requiring a repair or othermaintenance action. Without using sensor data, inspection intervals areoften set according to the interval T 606 determined as described above.

Maintenance optimization services help optimize an operator'smaintenance program based on scheduled and unscheduled maintenance data,combined with the sensor data 422. SBAS uses real-time sensor data, forexample, tire pressure, oxygen pressure, hydraulic fluid, auxiliarypower unit (APU), and engine oil levels, to offer diagnostic andprognostic services. The diagnostic and prognostic capabilities in SBASare leveraged to convert unscheduled maintenance into scheduledmaintenance, while also optimizing certain inspection task intervals. Inone embodiment, the inspection tasks whose findings are perfectlypredicted by the sensor data 422 are performed virtually, whereas, ifthe sensor data 422 is only a partial predictor of maintenance taskfindings, both virtual and locational inspection of tasks are performed.Thus, the actual locational inspection interval is escalated accordingto the predictive value of the sensor data 422.

FIG. 7 illustrates a changed inspection interval as related to the CDFcurve 602, enabled by the advantageous use of the sensor data 422. Whensensors are perfect predictors of failure or degradation, the optimalinspection interval approached infinity because inspections are nolonger required. However, physical limitations instead mean that sensorsare partial predictors of failure or degradation. This permits takingadditional risk, relative to relying only on physical inspections, asshown using a new acceptable risk level indicated by line 704 above theline 604. Where the line 704 intersects the CDF curve 602, indicates anew inspection interval T′ (T prime) 706 in units of time, which occurslater (a longer interval) than the interval T 606. Line 604 indicatesthe cumulative probability of a physical inspection identifyingnon-routine finding, and it is set at that the acceptable risk level.Line 704 indicates the cumulative probability of a physical inspection,a sensor alert, or both identifying non-routine finding at the priorinspection interval, T. Because, in some implementations, a sensor alertimproves the cumulative probability at a given interval, the intervalcan be escalated (lengthened) to T′ while still preserving the sameacceptable risk level.

FIG. 8 illustrates graphically how the changed threshold of FIG. 7 isdetermined. The CDF curve 602 for the cumulative probability ofidentifying non-routine findings, which includes degradation andfailures, is used along with the interval T 606 to determine the targetacceptable risk level indicated by the line 604. The probability, P,that a sensor alert predicts a potential failure is determined, such asby using the historical maintenance data 420. Since there is aprobability P that a sensor alert predicts the potential failure, aspecial construct for an equivalent risk level, R equivalent, enablesestimation of the risk that would be incurred, with an extended intervalin the absence of sensor data. In actual operation, the resulting risk,with the extended interval and the use of the sensor data, does notchange. R_equivalent is related to the original target acceptable risklevel R and probability P by:

R_equivalent 32 R+(1−R)×P   Equation 1:

Essentially, the sensor alert addresses the additional (1−R)×P risk,retaining the effective risk level, R_equivalent, as the original targetacceptable risk level R. The equivalent risk level, (R_equivalent) isindicated by the line 704.

P(J∪S)=P(J)+P(S)−P(J∩S)   Equation 2:

P(J∪S)=P(J)+P(S)−P(J|S)×P(S); per Baye's equation   Equation 3:

P(J∪S)=P(J)+P(S)−P(J)×P(S); since J and S operate independently  Equation 4:

P(J∪S)=P(J)+(1−P(J))×P(S)   Equation 5:

Where P(J∪S) is the probability of a physical inspection, J, or Sensor,S, or both detecting a non-routine finding, P(J) is the probability of aphysical inspection, J, alone detecting a non-routine finding, and P(S)is the probability (correct detection) of a sensor, S, alone detecting anon-routine finding. The point where the line 704 intersects the CDFcurve 602, this indicates the new inspection interval T′ 706 in units oftime. An undetected failure probability distribution curve 812represents the likelihood of a failure occurring that had not beenpredicted by a sensor alert. The original target acceptable risk levelindicated by the line 604 intersects the undetected failure probabilitydistribution curve 812 at the position corresponding to the newinspection interval T′ 706, showing how the sensor permits increasingthe inspection interval (from the T 606 to the T′ 706) while maintainingthe original target acceptable risk level. In some implementations, ifthe probability of detection by a sensor is random, with a knowndistribution (e.g., a Gaussian distribution), then a convolution methodis used to estimate the risk mitigated by use of the sensor data 422.

As an illustrative example, a maintenance task reads: “General visualinspection of the engine oil filter element bypass condition,” with theinterval, T, of 150 flight hours. The initial interval, T, of 150 hoursis derived from the CDF curve 602 with an acceptable risk level, R, of0.4. A sensor continuously measures the oil filter element bypasscondition. If the oil filter element bypass passes a certain threshold,the sensor generates an alert from the sensor. Two cases are presented:

Case 1: perfect sensor with P(S)=1. If there is a fault, the sensor willcatch it with 100% certainty. In this case Equation 1 becomes:

R_equivalent=R+(1−R)×P(S)=0.4+0.6×1   Equation 6:

The new physical inspection interval is pushed out indefinitely. Thatis, the inspection task is removed from physical inspection, and onlyvirtual inspection is needed.

Case 2: imperfect sensor with P(S)=0.8. If there is a fault, the sensorwill catch it with 80% certainty. In this case Equation 1 becomes:

R_equivalent=R+(1−R)×P(S)=0.4+0.6×0.8=0.88   Equation 7:

The new physical inspection interval is escalated, although notindefinitely.

FIG. 9 is a block diagram of an implementation of a process flow 900 forimplementing aspects of the disclosure. Input data includes the AMP 414and the maintenance optimization objectives 418, which together specifytasks, intervals, and acceptable risk; SBAS data and alerts 902 which isa portion of the sensor data 422; fault isolation information 904;historical scheduled maintenance data 906; and historical unscheduledmaintenance data 908. The historical scheduled maintenance data 906 andthe historical unscheduled maintenance data 908 are sourced from thehistorical maintenance data 420. A data acquisition and preprocessingstage includes using the data acquisition and the pre-processingcomponent 432 to acquire and/or process the SBAS data and alerts 902with a process 932 a, the fault isolation information 904 with a process932 b, and the historical scheduled maintenance data 906 and thehistorical unscheduled maintenance data 908 with a process 932 c. Theseresults are fed into the data mapping component 424. A task and alertselection process 910 selects various maintenance tasks as candidatesfor removal from physical inspection and/or escalation. Each task isthen fed into the analysis component 428. The analysis component 428then makes a decision in decision operation 912 whether to keep theoriginal interval at 914, remove the task from physical inspection at916 (thereby making it a virtual inspection), or escalate the intervalat 918. In some implementations, the analysis component 428 constructsdata to be used for generating the undetected failure probabilitydistribution curve 812.

The decision operation 912 uses the predictive value of the sensor data422 and the AMP 414 and the maintenance optimization objectives 418. Ifthe sensor data 422 has no predictive value, that is failures andnon-routine findings cannot be predicted by any of the sensor data 422or alert, then the original AMP interval is retained. If the sensor data422 has complete predictive value, that is failures and non-routinefindings are reliably predicted by the sensor data 422 or alerts, thenthe maintenance task, such as an inspection, is removed from physicalinspection. However, if the sensor data 422 has partial predictivevalue, that is failures and non-routine findings are predictable by thesensor data 422 or alerts with a reasonable probability, then themaintenance task is escalated, according to the reliability of thedetection. In this way, the SDOMP 430 is generated based at least on thepredictive value of the sensor data 422 and the maintenance optimizationobjectives 418. In some implementations, the SDOMP 430 includes anadjusted (e.g., escalated or de-escalated) interval, and also reflectsremoved tasks and some unchanged AMP intervals.

FIG. 10 is an implementation of a condition in which SBAS data andalerts from different sensors (e.g., sensors produced by differentcomponent suppliers), stored within the SBAS data and alerts 902, are indifferent formats. For example, alert data 1011 and 1012 from sensors1001 and 1002, respectively, are in a first format 1021. Alert data 1013and 1014 from sensors 1003 and 1004, respectively, are in a second,different format 1022. This illustrates the need for the dataacquisition and pre-processing component 432 to be able to receive datafrom different sources, in different formats, and process it asnecessary for downstream consumption. The SBAS data and alerts 902includes data from sensors provided by different component vendors andcome from different sources, and thus are not consistently formatted.

FIG. 11 is a block diagram 1100 of an implementation of a sensor mappingoperation. Operation 1102 is a portion of the task and alert selectionprocess 910, in which candidate SBAS alerts are identified. In someimplementations, operation 1102 includes adjusting an alert threshold(e.g., the threshold 506 of FIG. 5) in order to find an optimum alertthreshold. If a sensor alert provides too many false positives that arerelied upon, the resulting unscheduled inspections will increasemaintenance costs. In some example, the sensor itself is adjusted tochange the alert conditions to a lower false positive rate. This is usedwith the fault isolation information 904 which, in some implementations,is sourced from fault isolation manuals provided by component vendors.These data sets are then analyzed with recommended maintenance actions1104.

Also, maintenance tasks 1106, which in some implementations is derivedfrom the historical maintenance data 420, are used to identifyunscheduled maintenance task information. Unscheduled maintenance taskcandidates 1108 are selected, which provides failure information forvarious components and data on non-routine findings. Data regarding theunscheduled maintenance task candidates 1108 are also derived from thehistorical maintenance data 420, in some implementations. Themaintenance actions 1110 are then compared with the recommendedmaintenance actions 1104 to map the sensor data 422 to maintenancefindings at 1112. In some implementations, logical mapping of sensordata (including sensor alerts) to maintenance actions is followed byanother stage of using historical data to ascertain whether the numberof false alarm is too high.

FIG. 12 is an illustration of a timeline 1200 for related events, afirst event 1202 and a second event 1204, relevant in generating theSDOMP 430. The first event 1202 is a maintenance message, for example asensor alert or other reported sensor data, followed by the second event1204 after an interval 1206. The second event 1204 is a non-routinefinding, identified as non-routine finding X. Initially, it is possiblethat a correlation between the first event 1202 and the second event1204 is not recognized. However, during a mapping operation, acorrelation between the first event 1202 and the second event 1204 isidentified. For example, if significant number of non-routine findingshad been preceded by a relevant maintenance message, it is oftenpossible to establish a correlation.

FIG. 13 illustrates relationships between maintenance tasks (vianon-routine findings) and maintenance messages in a notional temporalrelationship network 1300. FIG. 13 provides an additional perspectivefor understanding the block diagram 1100 of FIG. 11. In some example,the sensor data 422 (whether alerts or generic sensor data) does not mapone-to-one with maintenance tasks (such as inspections) and/ornon-routine findings. In embodiments, there is a one-to-manyrelationship, a many-to-one relationship, and combinations, withoverlap. For example, multiple sensor alerts correlate with a firstnon-routine finding, and one or more of those multiple sensor alertsadditionally correlate with a plurality of other non-routine findings.

In general, the sensor data 422 brings two considerations: probabilityof detection (with the accompanying probability of false alarm), andrelevance. The first consideration, probability of detection, is areliability consideration, addressing the question of how reliably thesensor data 422 predicts a non-routine finding. It is often possible toestablish relevance with a statistical analysis that calculates acorrelation. A correlation is a mathematical specification ofrelatedness between two events. One correlation method, used in someimplementations, is a Pearson correlation coefficient (PCC), which is ameasure of the linear correlation between two variables X and Y. Aperfect correlation indicates that two events always occur together,whereas with uncorrelated events, when one event occurs, there is noprobative value in predicting whether a second event will occur. Thesensor data 422 is analyzed for reliability and relevance to maintenancetasks, non-routine findings, and maintenance actions.

The notional temporal relationship network 1300 answers questions suchas: What is the relevance of a particular sensor or set of sensors? Towhat extent is the sensor data 422 trustworthy? What leverage isprovided by the sensor data 422 for improving predictive value? Forexample, the significance of maintenance messages 1302 is identifiedusing the fault isolation information 904. The maintenance tasks 1106are used to identify non-routine findings 1308. A relationship is thenfound between the fault isolation information 904 and the maintenanceactions 1110, and a relationship is the found between the non-routinefindings 1308 and the maintenance actions 1110. This then completes amapping between the maintenance messages 1302 and the maintenance tasks1106, to facilitate quantification of the correlation betweenmaintenance findings and relevant sensor alerts and other sensor data.

FIGS. 14A-D illustrate the net benefit for various scenarios ofleveraging sensor data. Referring back to FIG. 8, a set of example casesis described, in which the physical inspection interval is T. T is foundas the inverse function (plotted as the CDF curve 602) of the risk, R,and T′ is the escalated interval. Thus, the number of scheduledmaintenance actions i 1/T and 1/T′ per unit time, respectively, for theoriginal and escalated intervals. The benefit of escalation, B, iscalculated by:

B=(1/T−1/T′)×C_scheduled   Equation 8:

Where C_scheduled is the cost of the scheduled maintenance for the task.

FIG. 14A shows a bar graph 1400 for a Case 1: An airline has a processto deal with a sensor based alert. Thus, there is already a businessprocess in place to handle inspections that are triggered by an alert,and so the cost of inspection is not included. The net benefit of SBASescalation, as shown in Equation 8, is B. In the bar graph 1400, a bar1402 shows the original cost of scheduled maintenance with interval T. Abar 1404 shows the new cost per unit of time of scheduled maintenancewith interval T′, and the benefit, B, is shown as a difference 1408.

FIG. 14B shows a bar graph 1410 for a Case 2.1: An airline does not havea process to deal with a sensor based alert, and the sensor thresholdcannot be changed to reduce false alarms. The net benefit of SBASescalation, as shown in Equation 9, is B:

B=(1/T−1/T′)×C_scheduled−P _(FA) ×C_unscheduled   Equation 9:

Where PFA is the probability of a false sensor alert, and C_scheduled isthe cost of the unscheduled maintenance for the task. In the bar graph1410, a bar 1416 shows the cost of unscheduled maintenance with intervalT′, so it is added atop the bar 1404. The net benefit, B, is shown as adifference 1418. If the sensor false alarms so much that the net benefitis negative (e.g., more money is spent), then the sensor data should notbe used to escalate the maintenance interval.

FIG. 14C shows a bar graph 1420 for a Case 2.2, which is similar to Case2.2, except it is possible to change the sensor alert threshold (e.g.,the threshold 506 of FIG. 5) to reduce PFA. Initially, the PFA and Cunscheduled create an unscheduled maintenance cost, shown as a bar 1426atop bar 1404, that is so high, the net benefit is negative. Thus, thesensor alert threshold is adjusted to reduce PFA. This has the effect ofdecreasing probability of detection, P(S), so the new interval will beT″ with the lower P(S).

FIG. 14D shows a bar graph 1430, also for Case 2.2, but reflecting thenew interval T″. In the bar graph 1430, a bar 1434 shows the new cost ofscheduled maintenance with interval T″, which is higher (reflectinghigher cost) than the bar 1404 in FIGS. 14A-C. A bar 1436, atop the bar1434, shows the unscheduled maintenance cost, due to C_unscheduled andthe reduced P_(FA). The net benefit, B, is shown as a difference 1438.The bar graph 1430 demonstrates the scenario where there exists an alertthreshold where the combination of P_(FA) and P_(D) (probability of acorrect detection and alert) yields a net benefit greater than zero.Together, FIG. 14A-D demonstrate that, although reliance on the sensordata 422 reduces maintenance events when the probability of false alarmsis sufficiently low, when the probability of false alarms is too high,reliance on the sensor data 422 unnecessarily increases maintenanceevents, thereby potentially increasing operator costs.

FIG. 15 is provides a flow chart 1500 illustrating an implementation ofan operation of determining whether to escalate a maintenance task. Theflow chart 1500 provides further explanation of the decision operation912. Selected historical sensor data 422 a, which is a subset of thesensor data 422, is combined with the historical maintenance data 420 ina correlation operation 1502 that, in some implementations, isrepresented by the notional temporal relationship network 1300. Decisionoperation 1504 determines whether the sensor data 422 could havepredicted a failure or other non-routine finding. If not, then theresult is the decision 914 to retain the original maintenance interval.It should be noted that the determination in the decision operation 1504is unlikely to be a clear yes or no result. In one example, the decisionis premised on a predictive value calculation that rarely reaches 0.0,but instead has low values for the no result. That is, in someimplementations, the predictive value is compared with a threshold toprovide the yes or no result.

If the sensor data 422 could have predicted the failure or non-routinefinding, then decision operation 1506 determines whether the sensor data422 could have fully predicted the non-routine finding (e.g.,degradation or failure or other non-routine finding). If yes, then theresult is the decision 916 to remove the maintenance task from physicalinspection and use virtual inspection (via sensors). Similarly to thedecision operation 1504, some implementations of the decision operation1506 use a second threshold, rather than requiring an absolutely perfectsensor data predictive value. If the sensor data 422 is not fullypredictive, then the sensor data 422 could have only partially predictedthe failure or other non-routine finding (reducing, rather thaneliminating risk), and the result is the decision 918 to escalate themaintenance interval. Part of this process (escalating the maintenanceinterval) is the modification of the detection probability illustratedin FIG. 8. Referencing the threshold used in the decision operation1504, escalating an inspection interval occurs when the predictive valueof the sensor data 422 meets the threshold.

With reference now to FIG. 16, a flow chart 1600 illustrates animplementation of an operation for generating the SDOMP 430. In oneembodiment, the operations illustrated in FIG. 16 are performed by theanalysis component 428 providing instructions to the one or moreprocessors 402. Operation 1602 includes receiving a first maintenanceplan for an apparatus type, the first maintenance plan definingmaintenance tasks for the apparatus type and defining intervals at whichthe maintenance tasks are to be performed. In some implementations, thefirst maintenance plan comprises an AMP (e.g., the AMP 414). In someimplementations, the apparatus type is an airplane (e.g., the flyingmodule 201).

Operation 1604 includes receiving historical sensor data (e.g., from thesensor data 422); operation 1606 includes receiving historical scheduledmaintenance data (e.g., from the historical maintenance data 420), andoperation 1608 includes receiving historical unscheduled maintenancedata (e.g., from the historical maintenance data 420). In someimplementations, the received sensor data comprises SBAS alert data. Insome implementations, the SBAS alert data comprises at least one datatype selected from the list consisting of: tire pressure, oxygenpressure, hydraulic fluid, APU oil level, and engine oil level.

Operation 1610 includes correlating the historical sensor data with thehistorical scheduled maintenance data and the historical unscheduledmaintenance data to determine a predictive value of the sensor data 422.In some implementations, determining a predictive value of the sensordata 422 comprises determining a probability that a sensor alertcorresponds to a non-routine finding. Operation 1612 includes receivingan operation history for the apparatus and operation 1614 includesdetermining a net benefit of adjusting an interval. False alarms (falsepositives) and missed detections (false negatives) drive up maintenancecosts. See for example, the description of FIGS. 14C and 14D. In someimplementations, this cost information is weighted according to theprobability of false alarms and missed detections. For example, when afirst maintenance task is deferred, due to reliance upon a sensor, andthe result is that a second, more expensive maintenance task is thenneeded, then the cost of the second, more expensive maintenance task isused in the determined cost. Operations 1612 and 1614 use data from theother data and logic 434, and are further described in relation to FIGS.17-19.

Operation 1616 includes constructing the CDF curve 602 and, based atleast on the predictive value of the sensor data 422, generating theSDOMP (e.g., the SDOMP 430), the generated SDOMP 430 comprising anadjusted one of the defined intervals. In some implementations,generating the SDOMP 430 is based at least on the predictive value ofthe sensor data 422 and the maintenance optimization objectives 418comprises escalating an inspection interval when the predictive value ofthe sensor data 422 meets a threshold. In some implementations,generating the SDOMP 430 further comprises generating the SDOMP 430based at least on the operation history for the apparatus. In someimplementations, generating the SDOMP 430 further comprises generatingthe SDOMP 430 based at least on the end date. In some implementations,generating the SDOMP further comprises generating the SDOMP 430 based atleast on the cost associated with the maintenance tasks. Operation 1618then includes presenting the generated SDOMP 430, for example on thepresentation component 404.

FIG. 17 is a block diagram illustrating a process 1700 for the analysiscomponent 428 to generate the SDOMP 430. The apparatus data 412 and theAMP 141 are used by the analysis component 428 in the operation 1602;and the historical maintenance data 420 is used in the operations 1606and 1608. The sensor data 422 (such as the historical sensor data) isused by the analysis component 428 in the operation 1604; and theresults of the operations 1604, 1606, and 1608 are used in the operation1610. The analysis component 428 uses the other data and logic 434 inthe operations 1612 and 1614. In some implementations, the other dataand logic 434 comprises the operation history for the apparatus, the enddate to the SDOMP 430 (e.g., the lease end date), and data fordetermining a net benefit of adjusting an interval (e.g., using themaintenance costs 416). The results of operations 1602, 1610, 1612 and1614 are used in operation 1616 to generate the SDOMP 430.

FIG. 18 is a block diagram of a system 1800 for generating an SDOMP. Thesystem 1800 is one implementation of a suitable computing environmentand is not intended to suggest any limitation as to the scope of use orfunctionality of the invention. Neither should the system 1800 beinterpreted as having any dependency or requirement relating to any oneor combination of components/modules illustrated.

The implementations and embodiments disclosed herein are described inthe general context of computer code or machine-useable instructions,including computer-executable instructions such as program components,being executed by a computer or other machine, such as a personal dataassistant or other handheld device. Generally, program componentsincluding routines, programs, objects, components, data structures, andthe like, refer to code that performs particular tasks, or implementparticular abstract data types. The disclosed implementations arepracticed in a variety of system configurations, including personalcomputers, laptops, smart phones, mobile tablets, hand-held devices,consumer electronics, specialty computing devices, etc. The disclosedimplementations are also practiced in distributed computingenvironments, where tasks are performed by remote-processing devicesthat are linked through a communications network.

The system 1800 includes a computing device (e.g., the computing device400) communicatively coupled to a network 1818. The computing device 400includes a bus 1816 that directly or indirectly couples the followingdevices: the memory area 410, the one or more processors 402, the one ormore presentation components 404, input/output (I/O) ports 1808, I/Ocomponents 1810, a power supply 1812, and a network component 1814. Thesystem 1800 should not be interpreted as having any dependency orrequirement related to any single component or combination of componentsillustrated therein. While the system 1800 is depicted as a seeminglysingle device, in one embodiment, multiple computing devices worktogether and share the depicted device resources. For instance, in oneembodiment, the memory area 410 is distributed across multiple devices,the processor(s) 402 provided are housed on different devices, and soon.

The bus 1816 represents one or more busses (such as an address bus, databus, or a combination thereof). Although the various blocks of FIG. 18are shown with lines for the sake of clarity, in reality, delineatingvarious components is not so clear, and metaphorically, the lines wouldmore accurately be grey and fuzzy. For example, one considers apresentation component such as a display device to be an I/O component.Also, processors have memory. Such is the nature of the art, and thediagram of FIG. 18 is merely illustrative of a system or computingdevice used in connection with one or more embodiments of the presentdisclosure. Distinction is not made between such categories as“workstation,” “server,” “laptop,” “hand-held device,” etc., as all arecontemplated within the scope of FIG. 18 and the references herein to a“computing device.”

In one embodiment, the memory area 410 includes any of thecomputer-readable media discussed herein. In one embodiment, the memoryarea 410 is used to store and access instructions configured to carryout the various operations disclosed herein. In some implementations,the memory area 410 includes computer storage media in the form ofvolatile and/or nonvolatile memory, removable or non-removable memory,data disks in virtual environments, or a combination thereof

In one embodiment, the processor(s) 402 includes any quantity ofprocessing units that read data from various entities, such as thememory area 410 or the I/O components 1810. Specifically, theprocessor(s) 402 are programmed to execute computer-executableinstructions for implementing aspects of the disclosure. In oneembodiment, the instructions are performed by the processor, by multipleprocessors within the computing device 400, or by a processor externalto the computing device 400. In some implementations, the processor(s)402 are programmed to execute instructions such as those illustrated inthe flowcharts discussed below and depicted in the accompanyingdrawings. Moreover, in some implementations, the processor(s) 402represent an implementation of analog techniques to perform theoperations described herein. For example, the operations are performedby an analog client computing device and/or a digital client computingdevice.

The presentation component(s) 404 present data indications to anoperator (e.g., owner of the apparatus or fleet of the apparatus,administrator responsible for the apparatus or fleet of the apparatus,or a user operating the apparatus) or to another device. In oneimplementation, presentation components include a display device,speaker, printing component, vibrating component, etc. One skilled inthe art will understand and appreciate that computer data is presentedin a number of ways, such as visually in a graphical user interface(GUI), audibly through speakers, wirelessly between the computing device400, across a wired connection, or in other ways.

The ports 1808 allow the computing device 400 to be logically coupled toother devices including the I/O components 1810, some of which is builtin. Implementations of the I/O components 1810 include, for example butwithout limitation, a microphone, keyboard, mouse, joystick, game pad,satellite dish, scanner, printer, wireless device, etc.

In some implementations, the network component 1814 includes a networkinterface card and/or computer-executable instructions (e.g., a driver)for operating the network interface card. In one embodiment,communication between the computing device 400 and other devices occurusing any protocol or mechanism over any wired or wireless connection.In some implementations, the network component 1814 is operable tocommunicate data over public, private, or hybrid (public and private)using a transfer protocol, between devices wirelessly using short rangecommunication technologies (e.g., near-field communication (NFC),Bluetooth® branded communications, or the like), or a combinationthereof

Although described in connection with the computing device 400,implementations of the disclosure are capable of implementation withnumerous other general-purpose or special-purpose computing systemenvironments, configurations, or devices. Implementations of well-knowncomputing systems, environments, and/or configurations that are suitablefor use with aspects of the disclosure include, but are not limited to,smart phones, mobile tablets, mobile computing devices, personalcomputers, server computers, hand-held or laptop devices, multiprocessorsystems, gaming consoles, microprocessor-based systems, set top boxes,programmable consumer electronics, mobile telephones, mobile computingand/or communication devices in wearable or accessory form factors(e.g., watches, glasses, headsets, or earphones), network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, VR devices,holographic device, and the like. Such systems or devices accept inputfrom the user in any way, including from input devices such as akeyboard or pointing device, via gesture input, proximity input (such asby hovering), and/or via voice input.

In one embodiment, implementations of the disclosure are described inthe general context of computer-executable instructions, such as programmodules, executed by one or more computers or other devices in software,firmware, hardware, or a combination thereof. In one embodiment, thecomputer-executable instructions are organized into one or morecomputer-executable components or modules. Generally, program modulesinclude, but are not limited to, routines, programs, objects,components, and data structures that perform particular tasks orimplement particular abstract data types. In one embodiment, aspects ofthe disclosure are implemented with any number and organization of suchcomponents or modules. For example, aspects of the disclosure are notlimited to the specific computer-executable instructions or the specificcomponents or modules illustrated in the figures and described herein.Other implementations of the disclosure include differentcomputer-executable instructions or components having more or lessfunctionality than illustrated and described herein. In implementationsinvolving a general-purpose computer, aspects of the disclosuretransform the general-purpose computer into a special-purpose computingdevice when configured to execute the instructions described herein.

By way of example and not limitation, computer readable media comprisecomputer storage media and communication media. Computer storage mediainclude volatile and nonvolatile, removable and non-removable memoryimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules, orthe like. Computer storage media are tangible and mutually exclusive tocommunication media. Computer storage media are implemented in hardwareand exclude carrier waves and propagated signals. Computer storage mediafor purposes of this disclosure are not signals per se. In oneimplementation, computer storage media include hard disks, flash drives,solid-state memory, phase change random-access memory (PRAM), staticrandom-access memory (SRAM), dynamic random-access memory (DRAM), othertypes of random-access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), flashmemory or other memory technology, compact disk read-only memory(CD-ROM), digital versatile disks (DVD) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other non-transmission medium used tostore information for access by a computing device. In contrast,communication media typically embody computer readable instructions,data structures, program modules, or the like in a modulated data signalsuch as a carrier wave or other transport mechanism and include anyinformation delivery media.

The following paragraphs describe further aspects of the disclosure:

-   A1. A method for generating an SDOMP, the method comprising:-   receiving a first maintenance plan for an apparatus type, the first    maintenance plan defining maintenance tasks for the apparatus type    and defining intervals at which the maintenance tasks are to be    performed;-   receiving historical sensor data, historical scheduled maintenance    data, and historical unscheduled maintenance data;-   correlating the historical sensor data with the historical scheduled    maintenance data and the historical unscheduled maintenance data to    determine a predictive value of the sensor data;-   receiving one or more maintenance optimization objectives of an    operator of an apparatus of the apparatus type; and based at least    on the predictive value of the sensor data and the maintenance    optimization objectives, generating the SDOMP, the generated SDOMP    comprising an adjusted one of the defined intervals.-   A2. The method of A1, wherein the first maintenance plan comprises    an approved maintenance plan.-   A3. The method of A1, wherein the apparatus type is an airplane.-   A4. The method of A1, wherein the sensor data comprises airplane    health management (AHM) alert data, and wherein the AHM alert data    comprises at least one data type selected from the list consisting    of:-   tire pressure, oxygen pressure, hydraulic fluid, auxiliary power    unit (APU) oil level, and engine oil level.-   A5. The method of A1, wherein determining a predictive value of the    sensor data comprises determining a probability that a sensor alert    corresponds to a non-routine finding.-   A6. The method of A1, wherein the one or more maintenance    optimization objectives of the operator comprises an acceptable risk    level.-   A7. The method of A1, wherein generating the SDOMP based at least on    the predictive value of the sensor data and the maintenance    optimization objectives comprises escalating an inspection interval    when the predictive value of the sensor data meets a threshold.-   A8. The method of A1, further comprising:-   receiving an operation history for the apparatus, and-   wherein generating the SDOMP further comprises generating the SDOMP    based at least on the operation history for the apparatus.-   A9. The method of A1, further comprising:-   determining an end date to the SDOMP, and-   wherein generating the SDOMP further comprises generating the SDOMP    based at least on the end date.-   A10. The method of A1, further comprising:-   determining a net benefit of adjusting an interval, and-   wherein generating the SDOMP further comprises generating the SDOMP    based at least on the cost associated with the maintenance tasks.-   A11. A system for generating an SDOMP, the system comprising:-   one or more processors; and-   a memory area storing an analysis component, that when executed by    the one or more processors, cause the one or more processors to    perform operations comprising:

receiving a first maintenance plan for an apparatus type, the firstmaintenance plan defining maintenance tasks for the apparatus type anddefining intervals at which the maintenance tasks are to be performed;

receiving historical sensor data, historical scheduled maintenance data,and historical unscheduled maintenance data;

correlating the historical sensor data with the historical scheduledmaintenance data and the historical unscheduled maintenance data todetermine a predictive value of the sensor data;

receiving one or more maintenance optimization objectives of an operatorof an apparatus of the apparatus type; and

based at least on the predictive value of the sensor data and themaintenance optimization objectives, generating the SDOMP, the generatedSDOMP comprising an adjusted one of the defined intervals.

-   A12. The system of A11, wherein the first maintenance plan comprises    an approved maintenance plan.-   A13. The system of A11, wherein the apparatus type is an airplane.-   A14. The system of A11, wherein the sensor data comprises AHM alert    data, and wherein the AHM alert data comprises at least one data    type selected from the list consisting of:-   tire pressure, oxygen pressure, hydraulic fluid, auxiliary power    unit (APU) oil level, and engine oil level.-   A15. The system of A11, wherein determining a predictive value of    the sensor data comprises determining a probability that a sensor    alert corresponds to a non-routine finding.-   A16. The system of A11, wherein the one or more maintenance    optimization objectives of the operator comprises an acceptable risk    level.-   A17. The system of A11, wherein generating the SDOMP based at least    on the predictive value of the sensor data and the maintenance    optimization objectives comprises escalating an inspection interval    when the predictive value of the sensor data meets a threshold.-   A18. The system of A11, wherein the operations further comprise:-   receiving an operation history for the apparatus, and-   wherein generating the SDOMP further comprises generating the SDOMP    based at least on the operation history for the apparatus.-   A19. The system of A11, wherein the operations further comprise:-   determining an end date to the SDOMP, and-   wherein generating the SDOMP further comprises generating the SDOMP    based at least on the end date.-   A20. The system of A11, wherein the operations further comprise:-   determining a net benefit of adjusting an interval, and-   wherein generating the SDOMP further comprises generating the SDOMP    based at least on the cost associated with the maintenance tasks.

When introducing elements of aspects of the disclosure or theimplementations thereof, the articles “a,” “an,” “the,” and “said” areintended to mean that there are one or more of the elements. The terms“comprising,” “including,” and “having” are intended to be inclusive andmean that there could be additional elements other than the listedelements. The term “implementation” is intended to mean “an example of”The phrase “one or more of the following: A, B, and C” means “at leastone of A and/or at least one of B and/or at least one of C.”

Having described aspects of the disclosure in detail, it will beapparent that modifications and variations are possible withoutdeparting from the scope of aspects of the disclosure as defined in theappended claims. As various changes could be made in the aboveconstructions, products, and methods without departing from the scope ofaspects of the disclosure, it is intended that all matter contained inthe above description and shown in the accompanying drawings shall beinterpreted as illustrative and not in a limiting sense.

What is claimed is:
 1. A method for generating a sensor driven optimizedmaintenance program (SDOMP), the method comprising: receiving a firstmaintenance plan for an apparatus type, the first maintenance plandefining maintenance tasks for the apparatus type and defining intervalsat which the maintenance tasks are to be performed; receiving historicalsensor data, historical scheduled maintenance data, and historicalunscheduled maintenance data; correlating the historical sensor datawith the historical scheduled maintenance data and the historicalunscheduled maintenance data to determine a predictive value of thesensor data; receiving one or more maintenance optimization objectivesof an operator of an apparatus of the apparatus type; and based at leaston the predictive value of the sensor data and the maintenanceoptimization objectives, generating the SDOMP, the generated SDOMPcomprising an adjusted one of the defined intervals.
 2. The method ofclaim 1, wherein the first maintenance plan comprises an approvedmaintenance plan.
 3. The method of claim 1, wherein the apparatus typeis an airplane.
 4. The method of claim 1, wherein the sensor datacomprises apparatus health management (AHM) alert data, and wherein theAHM alert data comprises at least one data type selected from the listconsisting of: tire pressure, oxygen pressure, hydraulic fluid,auxiliary power unit (APU) oil level, and engine oil level.
 5. Themethod of claim 1, wherein determining a predictive value of the sensordata comprises determining a probability that a sensor alert correspondsto a non-routine finding.
 6. The method of claim 1, wherein the one ormore maintenance optimization objectives of the operator comprises anacceptable risk level.
 7. The method of claim 1, wherein generating theSDOMP based at least on the predictive value of the sensor data and themaintenance optimization objectives comprises escalating an inspectioninterval when the predictive value of the sensor data meets a threshold.8. The method of claim 1, further comprising: receiving an operationhistory for the apparatus, and wherein generating the SDOMP furthercomprises generating the SDOMP based at least on the operation historyfor the apparatus.
 9. The method of claim 1, further comprising:determining an end date to the SDOMP, and wherein generating the SDOMPfurther comprises generating the SDOMP based at least on the end date.10. The method of claim 1, further comprising: determining a net benefitof adjusting an interval, and wherein generating the SDOMP furthercomprises generating the SDOMP based at least on the cost associatedwith the maintenance tasks.
 11. A system for generating a sensor drivenoptimized maintenance program (SDOMP), the system comprising: one ormore processors; and a memory area storing an analysis component, thatwhen executed by the one or more processors, cause the one or moreprocessors to perform operations comprising: receiving a firstmaintenance plan for an apparatus type, the first maintenance plandefining maintenance tasks for the apparatus type and defining intervalsat which the maintenance tasks are to be performed; receiving historicalsensor data, historical scheduled maintenance data, and historicalunscheduled maintenance data; correlating the historical sensor datawith the historical scheduled maintenance data and the historicalunscheduled maintenance data to determine a predictive value of thesensor data; receiving one or more maintenance optimization objectivesof an operator of an apparatus of the apparatus type; and based at leaston the predictive value of the sensor data and the maintenanceoptimization objectives, generating the SDOMP, the generated SDOMPcomprising an adjusted one of the defined intervals.
 12. The system ofclaim 11, wherein the first maintenance plan comprises an approvedmaintenance plan.
 13. The system of claim 11, wherein the apparatus typeis an airplane.
 14. The system of claim 11, wherein the sensor datacomprises apparatus health management (AHM) alert data, and wherein theAHM alert data comprises at least one data type selected from the listconsisting of: tire pressure, oxygen pressure, hydraulic fluid,auxiliary power unit (APU) oil level, and engine oil level.
 15. Thesystem of claim 11, wherein determining a predictive value of the sensordata comprises determining a probability that a sensor alert correspondsto a non-routine finding.
 16. The system of claim 11, wherein the one ormore maintenance optimization objectives of the operator comprises anacceptable risk level.
 17. The system of claim 11, wherein generatingthe SDOMP based at least on the predictive value of the sensor data andthe maintenance optimization objectives comprises escalating aninspection interval when the predictive value of the sensor data meets athreshold.
 18. The system of claim 11, wherein the operations furthercomprise: receiving an operation history for the apparatus, and whereingenerating the SDOMP further comprises generating the SDOMP based atleast on the operation history for the apparatus.
 19. The system ofclaim 11, wherein the operations further comprise: determining an enddate to the SDOMP, and wherein generating the SDOMP further comprisesgenerating the SDOMP based at least on the end date.
 20. The system ofclaim 11, wherein the operations further comprise: determining a netbenefit of adjusting an interval, and wherein generating the SDOMPfurther comprises generating the SDOMP based at least on the costassociated with the maintenance tasks.